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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# flake8: noqa E501
import shutil
import gradio as gr
from apscheduler.schedulers.background import BackgroundScheduler
from gradio_leaderboard import ColumnFilter, Leaderboard, SelectColumns
from huggingface_hub import snapshot_download
from src.about import (
CITATION_BUTTON_LABEL,
CITATION_BUTTON_TEXT,
EVALUATION_REQUESTS_TEXT,
EVALUATION_SCRIPT,
INTRODUCTION_TEXT,
LLM_BENCHMARKS_TEXT,
TITLE,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
BENCHMARK_COLS,
COLS,
EVAL_COLS,
EVAL_TYPES,
AutoEvalColumn,
ModelType,
Precision,
WeightType,
fields,
)
from src.envs import (
API,
CACHE_PATH,
EVAL_REQUESTS_PATH,
EVAL_RESULTS_PATH,
REPO_ID,
REQUESTS_REPO,
RESULTS_REPO,
TOKEN,
)
from src.populate import get_evaluation_requests_df, get_leaderboard_df
from src.submission.submit import add_new_eval
def restart_space():
API.restart_space(repo_id=REPO_ID)
# Space initialisation
shutil.rmtree(CACHE_PATH, ignore_errors=True)
try:
snapshot_download(
repo_id=REQUESTS_REPO,
local_dir=EVAL_REQUESTS_PATH,
repo_type="dataset",
tqdm_class=None,
etag_timeout=30,
token=TOKEN,
)
except Exception:
restart_space()
try:
snapshot_download(
repo_id=RESULTS_REPO,
local_dir=EVAL_RESULTS_PATH,
repo_type="dataset",
tqdm_class=None,
etag_timeout=30,
token=TOKEN,
)
except Exception:
restart_space()
LEADERBOARD_DF = get_leaderboard_df(
EVAL_RESULTS_PATH,
EVAL_REQUESTS_PATH,
COLS,
BENCHMARK_COLS,
)
(
finished_eval_requests_df,
running_eval_requests_df,
pending_eval_requests_df,
) = get_evaluation_requests_df(EVAL_REQUESTS_PATH, EVAL_COLS)
def init_leaderboard(dataframe):
if dataframe is None or dataframe.empty:
raise ValueError("Leaderboard DataFrame is empty or None.")
return Leaderboard(
value=dataframe,
datatype=[c.type for c in fields(AutoEvalColumn)],
select_columns=SelectColumns(
default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
label="Columns",
),
search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
filter_columns=[
ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Floating-point format"),
ColumnFilter(
AutoEvalColumn.params.name,
type="slider",
min=1,
max=500,
label="Number of parameters (billions)",
),
],
bool_checkboxgroup_label=' ',
interactive=False,
)
demo = gr.Blocks(css=custom_css)
with demo:
gr.HTML(TITLE)
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
with gr.Tabs(elem_classes="tab-buttons") as tabs:
with gr.TabItem("π Ranking", elem_id="llm-benchmark-tab-table", id=0):
leaderboard = init_leaderboard(LEADERBOARD_DF)
with gr.TabItem("π§ About", elem_id="llm-benchmark-tab-table", id=2):
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
with gr.Accordion(
"Evaluation script",
open=False,
):
gr.Markdown(
EVALUATION_SCRIPT,
elem_classes="markdown-text",
)
with gr.TabItem("π§ͺ Submissions", elem_id="llm-benchmark-tab-table", id=3):
with gr.Column():
with gr.Row():
gr.Markdown(EVALUATION_REQUESTS_TEXT, elem_classes="markdown-text")
with gr.Column():
with gr.Accordion(
f"β
Finished ({len(finished_eval_requests_df)})",
open=False,
):
with gr.Row():
finished_eval_table = gr.components.Dataframe(
value=finished_eval_requests_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
row_count=5,
)
with gr.Accordion(
f"β³ Pending ({len(pending_eval_requests_df)})",
open=False,
):
with gr.Row():
pending_eval_table = gr.components.Dataframe(
value=pending_eval_requests_df,
headers=EVAL_COLS,
datatype=EVAL_TYPES,
row_count=5,
)
with gr.Row():
gr.Markdown("# βοΈ Submission", elem_classes="markdown-text")
with gr.Row():
with gr.Column():
model_name_textbox = gr.Textbox(label="Model name")
revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
model_type = gr.Dropdown(
choices=[t.to_str(" ") for t in ModelType if t not in [ModelType.Unknown, ModelType.BrainDAO]],
label="Model type",
multiselect=False,
value=None,
interactive=True,
)
# precision = gr.Dropdown(
# choices=[i.value.name for i in Precision if i != Precision.Unknown],
# label="Precision",
# multiselect=False,
# value="float16",
# interactive=True,
# )
# weight_type = gr.Dropdown(
# choices=[i.value.name for i in WeightType],
# label="Weights type",
# multiselect=False,
# value="Original",
# interactive=True,
# )
# base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
submit_button = gr.Button("Submit")
submission_result = gr.Markdown()
submit_button.click(
add_new_eval,
[
model_name_textbox,
# base_model_name_textbox,
revision_name_textbox,
# precision,
# weight_type,
model_type,
],
submission_result,
)
# with gr.Row():
# with gr.Accordion("π Citation", open=False):
# citation_button = gr.Textbox(
# value=CITATION_BUTTON_TEXT,
# label=CITATION_BUTTON_LABEL,
# lines=20,
# elem_id="citation-button",
# show_copy_button=True,
# )
scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=900)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch(
server_name="0.0.0.0",
allowed_paths=["images/solbench.svg"],
)
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